TOP 100 Songs—A Spotify Experiment in Personality

By James Wallace Harris, Tuesday, May 3, 2016

If you selected your Top 100 all-time favorite songs, the ones that define your soul, how many of those songs would you think you shared with your friends? I’ve always loved seeing what albums my friends owned, and if they’d let me, what songs are on their playlists. People are surprisingly unique. I’ve yet to find anyone that shares even five favorite songs with me. Don’t get me wrong, me and my friends often enjoy the same kinds of music, but when it comes to absolute favorites, the songs we choose to form a life-long love affair, those tunes are quite distinctive. Maybe as identifying as fingerprints.

This is where Spotify comes in. It would be fantastic if Spotify created a permanent playlist in everyone’s account called TOP 100, and encouraged their subscribers to fill it in with the songs that define the music they loved best in their lifetime. Then after a time, start showing us big data statistics. What is the percentage of overlap based on various demographic standards. Am I more likely to overlap with other people born in 1951? Does gender matter? Could Spotify predict where I grew up or my ethnic background? Would it be possible for Spotify to discern my Myers-Briggs type? And if there are incidences of high overlap, would listening to the playlists of those subscribers help me find songs I would love that I’ve never heard?

Conversely, could Spotify fill in our TOP 100 lists automatically from studying our current patterns of play? Or predict our second 100 favorite songs?

Even with millions of users, would they ever find two people with the same songs in their TOP 100 playlist? What would be the statistical odds? (I don’t know, I can’t do that kind of math.) How often would 50% agreement show up? What if the list was based on order? If they applied statistical analysis to the data, would it reveal anything about personality? Would it tell us anything about generational shifts? Are people predictable by their tastes? If they could connect to other databases, would our musical tastes also reveal what we love in books, movies, television shows and other art forms?

My bet, which is only a hunch, would be for age cohorts, the average overlap would be less than 5%.